\begin{table}[H]
\begin{center}
\newcolumntype{C}{>{\centering\arraybackslash}X}

\caption{Descriptive statistics for Compustat data\label{table:summary_compustat}}
\begin{tabularx}{\textwidth}{lCCCC}

\toprule
{Statistic}&{Real $\text{sales}_{it}$}&{\% change in $\text{sales}_{i,t+1}$}&{$\text{markup}_{it}$}&{\% change in $\text{markup}_{i,t+1}$} \tabularnewline
\midrule\addlinespace[1.5ex]
Mean&1565.40&25.60&1.41&10.51 \tabularnewline
 P 25&27.57&-5.35&1.06&-3.25 \tabularnewline
 P 50&138.06&5.14&1.23&0.03 \tabularnewline
 P 75&648.47&18.76&1.51&3.33 \tabularnewline
 SD&8458.53&841.17&0.79&321.95 \tabularnewline
 
\bottomrule 

\end{tabularx}
\end{center}
\footnotesize{\emph{Notes:}  Each column of the table shows the summary statistics of the corresponding variable in the Compustat data. The dataset contains 242,155 observations for 20,252 firms across 67 years (1960-2016). Real $\text{sales}_{it}$ for firm $i$ and year $t$ are reported in millions. To calculate markups, we followed the procedure from \cite{de2020rise,traina2018aggregate}--we first estimated time-invariant but industry-specific (SIC 2-digits) output elasticities using the production function estimation method from \cite{de2020rise}. We then define $\text{markup}_{it}$ as $\text{output elasticities}_j$ times sales over cost of goods sold (COGS).
}
\end{table}
